Keyword creation method and its apparatus

ABSTRACT

A keyword creation method and its apparatus to simply create keywords in user&#39;s retrieving a desired item of information from a vast amount of information. The habitual situation characteristics and the degree of typical liking tendency of a user are calculated on the basis of answers on daily items of the user, typical situation dependent keyword(s) of the user in one or more individual typical situations previously prepared is (are) created in accordance with the degree of typical liking tendency of the user and typical situation dependent keyword(s) is (are) revised in accordance with the habitual situation characteristics of the user, so that keyword(s) according to the actual situation of the user can be created.

This is a Continuation of application Ser. No. 10/196,035, filed Jul.15, 2002, which is a Continuation of application Ser. No. 09/416,297,filed Oct. 14, 1999 now U.S. Pat. No. 6,430,560, which is a Continuationof application Ser. No. 08/980,268 filed Nov. 28, 1997 now U.S. Pat. No.5,970,486.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a keyword creation method and itsapparatus, and is appropriately applied, for example, to a programretrieval system for retrieving the programs necessary for a viewer frommany TV programs distributed via a broadcasting satellite.

2. Description of the Related Art

With a satellite broadcast system wherein TV programs are distributedvia a broadcasting satellite to viewer, TV signals are digitalized and avast number of programs are simultaneously distributed. In such asystem, the number of programs selected by a viewer increases markedly.

Besides, with a system for providing various items of information fromthe host side to computer terminals via a telephone line or privateline, a user on the terminal side selects the necessary item ofinformation from a vast amount of information and requests it to thehost side.

In an attempt to select such TV programs, items of information using acomputer or the like, a viewer or user must retrieve a desired programor information item from a vast number of programs or a vast amount ofinformation. In this case, a viewer or user selects a word or the likerelated to the genre of the program to be selected or the informationitem to be selected as the keyword and retrieves a desired program orinformation item by referring to it.

In a way of a viewer or user to directly input a keyword to a retrievalsystem, however, a viewer or user need to always learn and renew aknowledge concerning an up-to-date keyword or genre classificationmethod of information repeatedly according as programs or informationitems are renewed and has difficulty in readily selecting a desiredkeyword.

Besides, there is a method comprising storing keywords such as genres orwords selected by a viewer or user in the past as a selection historyand using them as keywords at the time of future retrieval. At a firsttime of using a retrieval system according to this method, no historyinformation is present and a viewer or user is compelled to directlyselect and input a keyword to the system and has such difficulty inreadily selecting a keyword as the above-mentioned case.

In such a manner, there was a problem that the retrieval operation of aviewer or a user is complicated and it is difficult to readily select arequired program or information item.

SUMMARY OF THE INVENTION

In view of the foregoing, an object of the present invention is toprovide a method and an apparatus for creating a keyword capable ofretrieving the information item corresponding to the liking of a vieweror user.

The foregoing object and other objects of the invention have beenachieved by inputting the answers of question items made to a user,calculating the habitual situation characteristics of the user and thedegree of a typical liking tendency of the user on the basis of theanswers, creating user's keyword(s) for typical situation(s) in one ormore previously prepared typical individual situation(s) on the basis ofthe degree of user's typical liking tendency and correcting thekeyword(s) on the basis of user's habitual situation characteristics,thereby creating the keyword(s) corresponding to user's actualsituation.

According to the present invention, on the user's input of daily itemssuch as his existing life stage and age/sex, his liking tendency andliving scene/selected site environmental phase, the keyword creationblock section creates the habitual situation conversion data related touser's habitual situation and the liking attribute ascribability datarelated to user's liking attribute, thereby automatically creating agroup of retrieval keywords reflecting the liking tendency of a userunder a specific situation in a specific field.

The nature, principle and utility of the invention will become moreapparent from the following detailed description when read inconjunction with the accompanying in which like parts are designated bylike reference numerals or characters.

BRIEF DESCRIPTION OF THE DRAWINGS

In the accompanying drawings:

FIG. 1 is a block diagram showing a satellite broadcast receiving systemusing a keyword creation unit according to the present invention;

FIG. 2 is a block diagram showing the configuration of an integratedreceiver/decoder (IRD) including the keyword creation unit;

FIG. 3 is a block diagram showing the keyword creation function block ofthe IRD;

FIGS. 4 to 7 are schematic diagrams showing an interaction screen to auser;

FIGS. 8A and 8B are schematic diagrams showing examples of habitualsituation conversion data;

FIG. 9 is a schematic diagram showing a simplified example of likingattribute space;

FIG. 10 is a schematic diagram showing an example of liking attributeascribability data array;

FIG. 11 is a schematic diagram showing an example of situation likingkeyword of a user; and

FIG. 12 is a schematic diagram showing a specific situation keywordgroup.

DETAILED DESCRIPTION OF THE EMBODIMENT

Preferred embodiment of the present invention will be described withreference to the accompanying drawings:

(1) General Configuration of a Satellite Broadcast Receiving System

In FIG. 1, 1 denotes a satellite broadcast receiving system as a whole,while a broadcast signal received by a parabolic antenna 3 isdemodulated and decoded in compression by an integrated receiver/decoder(IRD) 2. The resultant image/voice signal SV1 is delivered to thesubsequent video cassette recorder (VCR) 6 of VHS type.

The VCR 6 records an image/voice signal SV1 onto a video tape loadedinside or directly monitor-displays image/voice signal SV1 by deliveringit from an output line to a monitor device 4 as it is.

Besides, when a viewer manipulates a remote commander 5, the instructioncorresponding to the relevant manipulation is converted into an infraredsignal IR and delivered to the IRD 2. In accordance with the relevantinstruction, the IRD 2 executes various operations, such as channelswitching, registration/readout of user data and delivery of a controlsignal CONT to individual appliances (VCR 6, VCR 7, DVD 8 and MD 9)connected to the relevant IRD 2. A control signal CONT is delivered viaa control line to the VCR 6. If the VCR 6 is specified by this controlsignal CONT as the control object, the VCR 6 is controlled by therelevant control signal CONT. In contrast to this, if any of theappliances (VCR 7 of 8 mm type, digital video disc (DVD) player 8, minidisc (MD) player 9 and monitor device 4) successively connected to theVCR 6 via a control line is specified as the control object, the VCR 6delivers a control signal CONT to the subsequent VCR 7 of 8 mm type asit is.

On the input of a control signal CONT, the VCR 7 identifies theappliance specified by the control signal CONT. If the identified resultis the VCR 7, the VCR 7 executes the operation specified by the controlsignal CONT. If this direction is, for example, a direction for theplayback of an 8 mm video tape loaded on the VCR 7, the VCR 7 displaysit by the playback of the video tape and the delivery of a playbacksignal SV3 to the monitor device 4. Besides, if the direction by acontrol signal CONT is a direction for recording a broadcast signal(image/voice signal SV1) received and decoded by the IRD 2 in the VCR 7,the VCR 7 records the image/voice signal SV1 inputted from the IRD 2 viaa VCR 6 of VHS type and the monitor 4. In contrast to this, if thecontrol object of a control signal CONT is not the VCR 7, the VCR 7delivers the relevant control signal CONT to the subsequent DVD 8 as itis.

On the input of a control signal CONT, the DVD 8 identifies theappliance specified by the control signal CONT. If the identified resultis the DVD 8, the DVD 8 executes the operation specified by the controlsignal CONT. If this direction is, for example, a direction for theplayback of images or voices from the disk loaded on the DVD 8, the DVD8 displays it by the playback of the disk to deliver an image/voicesignal SV4 to the monitor device 4. In contrast to this, if the controlobject of a control signal CONT is not the DVD 8, the DVD 8 delivers therelevant control signal CONT to the subsequent MD 9 as it is.

On the input of a control signal CONT, the MD 9 identifies the appliancespecified by the control signal CONT. If the identified result is the MD9, the MD 9 executes the operation specified by the control signal CONT.If this direction is, for example, a direction for the playback of adisk loaded on the MD 9, the MD 9 gives off a voice signal from aspeaker (not shown) mounted in the monitor device 4 by the playback ofthe disk to deliver the voice signal SA1 to the monitor device 4.Besides, if the direction by a control signal CONT is a direction forrecording a voice signal SA2 in a broadcast signal received and decodedby the IRD 2 in the MD 9, the MD 9 records the voice signal SA2 inputtedfrom the IRD 2 via a VCR 6 of VHS type and the monitor device 4. Incontrast to this, if the control object of a control signal CONT is notthe MD 9, the MD 9 delivers the relevant control signal CONT to thesubsequent monitor device 4 as it is. At that time, the monitor device 4executes the operation specified by the relevant control signal CONT.

(2) Configuration of an IRD

In the IRD 2, as shown in FIG. 2, an RF signal outputted from the lownoise block downconverter (LNB) 3A of a parabolic antenna 3 is fed to atuner 21 in the front end 20 and demodulated. An output of the tuner 21is fed to a QPSK demodulator circuit 22 and QPSK-demodulated. An outputof the QPSK demodulator circuit 22 is fed to an error correction circuit23, whose errors are detected and corrected, and is amended ifnecessary.

In a conditional access module (CAM) 33 comprising an IC card made ofCPU, ROM and RAM, a cipher key is stored together with a decodedprogram. Since a signal transmitted via a broadcast satellite isenciphered, a key and cipher processing is required for deciphering thiscipher. Thus, this key is read out from the CAM 33 via a card readerinterface 32 and is fed to a demultiplexer 24. The demultiplexer 24deciphers an enciphered signal by using this key.

The demultiplexer 24 receives a signal outputted from the errorcorrection circuit 23 of the front end 20, feeds a deciphered videosignal to the MPEG video decoder 25 and feeds a deciphered audio signalto the MPEG audio decoder 26.

The MPEG video decoder 25 stores the inputted digital video signal inthe DRAM 25A and executes the decode processing of the video signalcompressed by the MPEG scheme. The decoded video signal is fed to anNTSC encoder 27 and converted into a brightness signal (Y), chromasignal (C) and composite signal (V) in the NTSC scheme. The brightnesssignal and chroma signal are outputted as S video signals via bufferamplifiers 28Y and 28C, respectively. Besides, the composite signal isoutputted via a buffer amplifier 28V.

The MPEG audio decoder 26 stores an audio digital signal fed from thedemultiplexer 24 in a DRAM 26A and executes the decode processing of anaudio signal compressed by the MPEG scheme. The decoded audio signal isdigital-to-analog converted in a D/A converter 30, the audio signal ofthe left channel is outputted via a buffer amplifier 31L and the audiosignal of the right channel is outputted via a buffer amplifier 31R.

An RF modulator 41 converts the composite signal outputted by the NTSCencoder 27 and the audio signal outputted by the D/A converter 30 intoRF signals and outputs them. Besides, this RF modulator 41 allows an RFsignal of NTSC scheme inputted from other appliances to pass through themodulator and outputs it to other appliances as it is.

In the case of this embodiment, these video and audio signals are fed tothe VCR 6 via an AV line.

The CPU 29 executes various processing in accordance with the programstored in the ROM 37. Besides, the CPU 29 controls an AV appliancecontrol signal transmitter/receiver section 2A, outputs a predeterminedcontrol signal to other appliances via a control line and receives acontrol signal from other appliances.

Directly inputted to this CPU 29 can be a predetermined instruction bymanipulating a manipulation button switch in the front panel 40.Besides, on the manipulation of a manipulation key in the remotecommander 5, an infrared (IR) signal is outputted by the IR transmittersection of the remote commander 5 and received by an IR receiver section39, and the received result is fed to the CPU 29. Accordingly, also bythe manipulation of the remote commander 5, a predetermined instructioncan be inputted to the CPU 29.

Besides, the CPU 29 takes in, for example, the electronic program guide(EPG) information except a video and an audio signal outputted from thedemultiplexer 24, makes out EPG data from it and feeds them to an staticrandom access memory (SRAM) 36 and stores them. The EPG informationincludes information items (such as e.g., channel, time, title and genreof a program and program comment) about the programs of individualchannels from the present time to tens of hours later. Since this EPGinformation item frequently comes by transmission, an up-to-date EPGinformation item is always retained in the SRAM 36

The CPU 29 can transfer the data stored n the SRAM 36 to an externalappliance via a modem 34 and communication means. Meanwhile, as a methodfor transferring data of the SRAM 36 to an external appliance (floppydisk, card like recording medium, or the like), an output lineexclusively for data may be provided in addition to the communicationusing a modem.

And, in an electrically erasable programmable read only memory (EEPROM)38, data desired to be retained even after the power off (rewritabledata, such as e.g., receiving history for the past 4 weeks of a tuner 21or data of the data base mentioned later (11A, 11B and 11C)) are stored.Besides, comparing the time information outputted by a calendar timer 35with the time stamp separated from a received signal and outputted bythe demultiplexer 24, the CPU 29 controls the MPEG video decoder 25 orMPEG audio decoder 26 so as capable of conducting a decode processing ata proper timing.

Furthermore, when wanting to generate predetermined on-screen display(OSD) data, the CPU 29 controls the MPEG video decoder 25. Correspondingto this control, the MPEG video decoder 25 creates and writespredetermined OSD data into a DRAM 25A and further reads out and outputsthem. Thereby, predetermined characters, pictures and such others can beoutputted and displayed in the monitor device 4.

Here, when the manipulation key for program guide is selected in theremote commander 5 or the front panel 40, the CPU 29 controls the MPEGvideo decoder 25 to display a broadcast program selection screen in themonitor device 4. By moving the cursor to the position of a desiredprogram on this screen and clicking the remote commander 5, a user canselect and specify the desired program. At this time, with that programgenre corresponding to the liking of a user taken as a keyword which hasbeen created in advance in a keyword creation function block provided inthe IRD 2, the list of programs fit for the relevant user is displayedfrom numbers of programs.

Like this, FIG. 3 shows the creation function block for a keywordemployed in the retrieval of the program desired by a user in accordancewith the EPG information. That is, in FIG. 3, the user interfaceprocessing section 12 corresponds to the remote commander 5, the IRreceiver section 39 and the front panel 40 in the IRD 2 (FIG. 2), theanswer analysis processing section 13, the situation dependent likingkeyword creating section 14, specific situation liking keyword creatingsection 15 and the package title retrieval processing section 16correspond to the CPU 29 (FIG. 2) and the liking sect cluster dictionary11A, the liking-sect-dependent, situation dependent keyword group database 11B and the package title data base 11C correspond to the EEPROM38.

(3) Creation of a Keyword by the IRD

FIG. 3 shows the functional block of the portion related to the creationof a keyword in the IRD 2 mentioned above referring to FIG. 2, and theuser interface processing section 12 displays an interaction screen forthe creation of a keyword on the display screen 4A of the monitor device4 (FIG. 1) by user's manipulation of the remote commander 5. Whilespecifying the answers for individual question items on this interactionscreen by using a cursor, a user inputs a user profile for the creationof a keyword.

These input items first of all include an item for the input of growthstages of a user individual, such as “Advance to a university”, “Takingemployment”, “Wedding”, “Bringing up of a child” and “Retirement”, inwhich the relation of a user with his family and society is consideredadditionally, as the present life stage of the user. In this case, aninteraction screen as shown in FIG. 4 is displayed on the display screen4A of the monitor device 4.

Secondly, the input items include an item for the input of an age/sex.In this case, an interaction screen as shown in FIG. 5 is displayed onthe display screen 4A of the monitor 4.

Thirdly, the input items include an item concerning the liking tendencyof a user. In this case, an interaction screen for specifying aplurality of liking tendencies as shown in FIG. 6 is displayed on thedisplay screen 4A of the monitor 4.

Fourthly, the input items include an item for the input of a livingscene such as “at breakfast”, “at lunch”, “at supper”, “at your ease ona weekday” and “at your ease on a holiday”, as living scene/select siteenvironmental phase of a user. In this case, a user inputs his ownactual time range (referred to as environmental numerical value/regiondata) corresponding to each living scene on an interaction screen asshown in FIG. 7 for each day of a week. As a result, data such as“7:00-7:30 of Monday”, “7:30-8:00 of Saturday”, . . . are obtained, forexample, as a living scene for “at breakfast”.

In such a manner, when a user's answer is inputted, the user interfaceprocessing section 12 delivers the answer to the answer analysisprocessing section 13. By pairing living scenes inputted by a user withindividual time frame identifiers (situation identifiers) represented bythe respective different identifiers and day-of-week time range data(region data of environmental numerical values) peculiar to the usercorresponding to individual time frame identifiers, obtained on thebasis of the answer of a user, for each living scene, the answeranalysis processing section 13 obtains the habitual situation conversiondata of the user.

FIGS. 8A and 8B show examples of these habitual situation conversiondata. That is, FIG. 8A comprises a data array with days of a week andtime made into correspondence to the time frame identifier (situationidentifier) representing “at breakfast”. In this case, since thebreakfast is taken in the same time range for a Monday to Friday, thesedata are represented by a product of data representing the range of daysof a week (Monday-Friday) and data representing the range of time(7:00-7:30) and further for Saturday where a breakfast is taken at adifferent time from that of these weekdays, they are represented by aproduct of data representing the range of the relevant day of a week(Saturday) and data representing the range of time (7:30-8:00). By a sumof individual data represented by such products of day-of-week rangedata and time range data, day-of-week/time range data (region data ofenvironmental numerical values) are obtained and habitual situationconversion data are obtained by a combination of these day-of-week/timerange data and time frame identifiers (situation identifiers).

Besides, FIG. 8B shows habitual situation conversion data comprising acombination of the time frame identifier (situation identifier)representing “at your ease on a holiday” and day-of-week/time/range dataand expresses that the living scene identifier of “at ease on a holiday”corresponds to the time range of 8:00-11:30 both for Saturday and forSunday. In such a manner, a time frame identifier as the situationidentifier established in conformity to the characteristics of a user isthe name or number for distinguishing a typical living scene affectingthe selection of a program, affects the selection of a programindependently of the liking tendency of a user and forms a factor to beselected in accordance with the relevant moments and cases.Incidentally, in addition to a time frame identifier, the situationidentifiers include, for example, a partner situation identifierestablished in accordance with partners common in situation to therelevant user and the common partners of situation include friends,lovers or the like. This partner situation identifier is employed in akeyword creation for the selection of a music program and musicsoftware.

Thus, habitual situation conversion data representing the custom of auser, evaluated by a combination of time frame identifiers and regiondata of environmental numerical values are stored once in the EEPROM 38(FIG. 2).

Besides, the answer analysis processing section 13 evaluates a likingattribute ascribability data array as data representing the likingtendency of a user that changes depends on time and situation. In thiscase, an item on liking tendency inputted by a user to the userinterface processing section 12 is employed. This item is one inputtedfrom the interaction screen mentioned above in relation with FIG. 6. Byanswers to this, a plurality of liking attributes such as “knowledgedirectionality”, “activeness directionality”, “amusement directionality”and “relaxation directionality” influential on the selection of aprogram are obtained as the sense of attitude value of a user for TVviewing. Incidentally, at the time of keyword creation for the selectionof a music, items for obtaining directional tendency such as “specificgenre directioned”, “piece notion directioned”, “wide sound rangedirectioned” and “trend directioned” are given as questions to a user.

Thus, first based on the answers of a user concerning the likingtendency inputted to the user interface processing section 12, theanswer analysis processing section 13 evaluates the liking attributes ofthe user. That is, the answer analysis processing section 13 establishesthe respective directionalities concerning liking attributes such as“knowledge directionality”, “activeness directionality”, “amusementdirectionality” and “relaxation directionality”, obtained by the answersof a user, as values indicating individual directionalities on theattribute classification axes. Thereby, on the liking attributeclassification space formed by individual liking attributeclassification axes, the coordinates determined by individualdirectionalities serve as liking attribute vectors of a user and onepoint on the liking space determined by this attribute vector becomesthe liking attribute point indicating the liking tendency of this user.

Incidentally, FIG. 9 shows one example of liking attributeclassification space formed by three attribute classification axes, agelevel axis (Z-axis), activeness direction axis (X-axis) and knowledgedirection axis (Y-axis), while the liking attribute point P is evaluatedfrom the age, activeness directionality and knowledge directionalityobtained from the input of the user.

Here, when a plurality of liking attribute points are plotted in oneliking attribute classification space with many users taken as thepopulation, there are cases where crowded collections (hereinafter,referred to as clusters) appear at several sites. The respectiveclusters correspond to collections of users having a similar likingattribute and a finite number of clusters are present in the likingattribute classification space which are not always exclusive. Theexamples of clusters include the knowledge attitude cluster CL1corresponding to a relaxed amusing sect, the knowledge attitude clusterCL2 corresponding to a knowledge desiring sect and the knowledgeattribute cluster CL3 corresponding to a trend pursuing sect asknowledge attitude clusters determined by the knowledge direction axis,the activeness direction axis and the age level axis shown in FIG. 9.Besides, there is also a case where clusters are formed in theprojection subspace using a part of the liking attribute classificationaxes. In this case, for example, age level clusters are formed in theprojection space using the age level axis.

Incidentally, in the liking attribute classification space for theselection of a music, clusters corresponding to a mood fascinating sect,a scream diverging sect and so on are formed.

The name or number employed for distinguishing these clusters arereferred to as a cluster identifier and the center of each cluster isreferred to as a cluster representative point. Here, the likingattribute point P corresponding to one user does not generally coincidewith the representative point of a cluster. Besides, one user isconsidered to have the liking attribute of the adjacent clusters to someextent. Thus, the degrees of the liking attribute of one user to beascribed to the respective adjacent clusters are expressed in anumerical array and this numerical array is defined as a likingattribute ascribability data array of the user.

Here, when data on the liking attribute point P of a user is settled,the degrees of ascribability to individual clusters are determined fromthe liking attribute point P and representative points, stretches andshapes of clusters. Among these, cluster representative points andstretches of clusters are not dependent on the liking attribute point Pof the user at all and peculiar to the respective clusters. Thus, from acluster representative point and a stretch aspect for each cluster, themethod for calculating the ascribability (liking attributeascribability) to the respective clusters can be determined in advance.

The method for calculating the ascribability (liking attributeascribability) to a cluster will be described below. To evaluate theascribability (liking attribute ascribability) to a certain cluster whenthe liking attribute point P of one user is settled, first, the errorvector between the liking attribute point P and the clusterrepresentative point is evaluated. Next, using a function thatmonotonously decreases with larger error vector (i.e., functiondepending on the stretch of a cluster), its value is calculated.

If the stretch aspect of the function employed for evaluating thisliking attribute ascribability is independent of individual likingattribute classification axis direction and isotropic, the inverse of1.0 plus the square of the length (representing the distance of astretch) of an error vector normalized by a standard deviation of astretch (stretch deviation) or the like is set to a liking attributeascribability. In this case, a city block distance, maximum absolutevalue component or Euclid distance may be employed as the length of anerror vector.

Alternatively, if the stretch of a cluster differs with individualliking attribute classification axes, the inverse of about 1.0 plus thesquare of a norm having an (rectangular parallelopiped) axis-dependentweight with the inverse of a standard deviation for each likingattribute classification axis taken as the weight coefficient for therelevant axis (i.e. when the cluster regarded as a rectangularparallelepiped) is set to the liking attribute ascribability in place ofthe above-mentioned isotropic distance.

Alternatively, if a cluster stretches in a direction slant to likingattribute classification axes, the quotient of a definite number byanother definite number plus the ellipsoid norm (i.e. when the clusterregarded as an ellipsoid) of quadratic form using the coefficientsevaluated from covariance coefficients or the like is set to the likingattribute ascribability.

Incidentally, when the stretch of a cluster is complicated and a generalfunction is necessary, a function wherein the convex polyhedron normusing the maximum of finite number of linear expressions is utilized inplace of the above-mentioned city block distance, a function using aneuro or lookup table or the like can be employed.

Various functions set as an ascribability calculation method in thismanner are previously stored in a cluster dictionary 11A (FIG. 3), dataspecified for this ascribability calculation method are the respectivefunctions used for individual clusters in calculating the ascribabilityof clusters and data for specifying what parameters to execute thesefunctions with, which are combinations of calculation functionidentifiers expressed in function pointer and calculation parameterssuch as cluster representative points and cluster stretch degree. Thecalculation parameters are expressed in a data array, pointers to datastructures or the like.

When the liking attribute point P of a user is settled by the analysisof user's answers in the answer analysis processing section 13,calculation of a liking attribute ascribability data array used for thefunctions and parameters set in such a manner is executed in the answeranalysis processing section 13 while referring to the ascribabilitycalculation method specified data corresponding to individual clustersstored in the cluster dictionary 11A.

That is, to calculate a value of ascribability to one cluster,ascribability calculation method specified data for the cluster arefetched from the cluster dictionary 11A, the function specified by therelevant ascribability calculation method specified data is read outwith parameters serving as a part of calculation method specified dataand liking attribute point data resulting from the answer analysis beingemployed as augments and is executed. A functional value obtained as anexecution result of this function is a cluster ascribability value. Asuccessive substitution of ascribability values, obtained bysuccessively repeating this for all clusters, into array componentswould provide the liking attribute ascribability data array of the user.

Incidentally, the cluster dictionary 11A is not only provided in theEEPROM 38 (FIG. 2), but can be also read in from a predeterminedrecording medium or downloaded from the communication line, stored inthe EEPROM 38 and used. In this case, the kind and calculation method ofclusters become updatable and further a new calculation scheme can beimplemented by updating a cluster dictionary together with theregistration and addition of a new function program.

Incidentally, FIG. 10 shows one example of liking attributeascribability data array, while in an array of ascribability to each agelevel, individual arrayed numerals represent the ascribability for therespective age levels (e.g., teens, twenties, thirties, . . . ) andindividual arrayed numerals in an array of ascribability to each likingsect represent the ascribability for the respective liking sects (e.g.,knowledge desiring sect, trend pursuing sect, . . . ). In this case, bylimiting individual arrayed numerals to “0” or “1”, a numeral signifiesa user either perfectly belonging to or completely being independent ofeach cluster.

In such a manner, when the liking attribute ascribability data array ofa user is obtained in the answer analysis processing section 13, therelevant attribute ascribability data array is delivered to thesituation dependent liking keyword creation section 14 (FIG. 3) togetherwith the above-mentioned habitual situation conversion data. Thesituation dependent liking keyword creation section 14 makes the likingattribute cluster corresponding to several highly ascribable members ofthe liking attribute ascribability data array into the stronglyascribable cluster of the user.

The situation dependent liking keyword creation section 14 fetches thekeyword corresponding to the relevant strongly ascribable cluster fromthe liking-sect-dependent, situation dependent keyword group database11B. In this liking-sect-dependent, situation dependent keyword groupdata base 11B, keywords included in liking titles (liking programgenres) under various situations, of persons of various tendencies areclassified and stored.

That is, generally, typical users ascribed to each liking cluster liketitles (program genres) of a definite tendency under a typicalsituation. Thus, in the liking-sect-dependent, situation dependentkeyword group database 11B, a group of keywords frequently appearing inliking titles (program genres) or news items for introduction/summary isprepared previously for each situation classification and for eachliking class. Incidentally, at the creation of a keyword in theselection of a TV program, a name of program genre is prepared as afrequent keyword.

To each keyword prepared in the liking-sect-dependent, situationdependent keyword group database 11B, a liking degree is attached.

If at least one liking attribute cluster is specified, thisliking-sect-dependent, situation dependent keyword group data base 11Bis so arranged that a group of keyword/liking degree pairs divided foreach situation classification can be fetched. As an actual construction,a data base, a retrieval server (subroutine, thread and process) and soon are utilized.

Accordingly, depending on a typical situation represented by eachsituation classification identifier, the situation dependent likingkeyword creation section 14 successively fetches the situation likingkeyword group of the user corresponding to his strongly ascribablecluster from the liking-sect-dependent, situation dependent keywordgroup data base 11B. In general, there are a plurality of stronglyascribable clusters, so that a plurality of liking keyword groups areobtained also for a single situation. They are merged (lumped) into aset for each situation. As this way of merge, first, a collection ofkeywords is obtained by the collection and merge of keyword groups foreach cluster. Next, a liking degree paired to each keyword is calculatedfrom the liking degree attached to a cluster keyword and theascribability to the cluster if the keyword comes from the likingkeyword group of a unique cluster. The functional conditions for thiscalculation is a function having a weak monotonously increasing propertyfor both the original liking degree and ascribability.

For example, there are a method using a product of the liking degree andascribability, a method using an arithmetic mean, a method using aminimum, etc. Furthermore, a monotonously increasing function byutilizing a lookup table technique may be employed.

Next, on the assumption that the same keyword is included in the likingkeyword group for a plurality of clusters, first, the liking degree isevaluated solely for each cluster in accordance with one of the methodsmentioned above and their sum or maximum is made into a synthesizedliking degree.

In such a manner, by repeating these processing for each situationclassification, the liking keyword group (program genre name group) foreach situation concerning a specific user is obtained.

The keyword group obtained thus is stored and retained in the EEPROM 38(FIG. 2). Besides, strongly ascribable cluster data for each user arealso stored and retained in the EEPROM 38 and if a liking keyword database for each liking cluster situation (liking-sect-dependent, situationdependent data base of FIG. 3) is updated, a liking keyword group foreach situation for each user can be updated by retrieving the updateddata base again and synthesizing it in accordance with the methodsmentioned above.

Incidentally, FIG. 11 shows one example of situation dependent likingkeywords created in the situation dependent liking keyword creationsection 14 and a program genre name group in each situation (atbreakfast, at rest, . . . ) is created for each situation.

In such a manner, the situation dependent liking keyword group (FIG. 11)created in the situation dependent liking keyword creation section 14 isdelivered to the subsequent specific situation liking keyword creationprocessing section 15. Here, the specific situation indicates thesituation at a specific time point and is typically represented by asituation identifier, but becomes a complex of situations represented bya plurality of situation identifiers according to individual situations.Thus, employed as the representation of specific situations is an arrayof numerical values, representing the degree of being close toindividual typical situations (situation ascribability) represented bysituation identifiers. This situation ascribability array will bereferred to as situation ascribability data array.

This situation ascribability data array can be automatically created bythe relevant system or can be inputted to the system on the spot by auser via input means (user interface processing section 12). Forexample, the degree of time frame ascribability for discriminating theboundary neighborhood of a time frame on the basis of time isautomatically created by the CPU 29 (FIG. 2). In contrast to this, withrespect to partner situation or the like on the site, the ascribabilityto the relevant situation is settled as a result of user's input tospecify a situation by using an interaction screen.

On the basis of situation dependent liking keyword groups correspondingto individual typical situations received from the situation dependentliking keyword creation section 14, the specific situation likingkeyword creation section 15 evaluates the liking keyword group of aspecific user corresponding to a specific situation expressed in asituation ascribability data array through the weighted synthesis usingan ascribability. In the weighted synthesis calculation for obtainingthe liking degree to be paired to each keyword, a product sum ofsituation ascribabilities and liking degrees for typical situations canbe simply employed. The keyword collection with liking degree obtainedthus becomes a specific situation liking keyword group of the specificuser. Incidentally, as a technique of weighted synthesis calculation forliking degree, a function that has a monotonuously increasing propertyconcerning all variables may be selected and employed for synthesis.

In such a manner, as shown in FIG. 12, the specific situation keywordgroup created in the specific situation liking keyword creationprocessing section 15 is delivered to the subsequent package titleretrieval processing section 16 and the corresponding title is retrievedfrom the package title data base 11C in accordance with the relevantspecific situation keyword group. With this embodiment, the EPG datatransmitted by a satellite broadcast is stored in the package title database 11C and the EPG data specified by a program genre created as aspecified situation keyword group is retrieved. On the display screen 4Aof the monitor device 4, a plurality of characters representing theprograms retrieved by these EPG data are displayed and a user can selectthe relevant program by specifying any of the relevant characters.

Incidentally, the content of the package title data base 11C is updatedfor each fetch of a new EPG data, thus always retaining up-to-date data.

(4) Operation and Effect of the Embodiment

In the above arrangement, when a user inputs daily items such as user'sexisting life stage, age/sex, user's liking tendency and user's livingscene/selected site environmental phase by means of an interactionscreen displayed on the monitor screen, the keyword creation blocksection (FIG. 3) of the IRD 2 creates habitual situation conversion datarelated to the habitual situation of the user and liking attributeascribability data related to the liking attribute of the user andthereby creates a keyword group for retrieval reflecting the likingtendency of the user under a specific situation in a specific field.

Thus, only if, even without a professional knowledge on retrieval suchas keywords always updated and up-to-date knowledge on genreclassification methods, a user answers a daily simple question on itemsrelated to habit and liking once, programs conforming to the situationpeculiar to the user and his liking are continuously retrieved from thattime.

Besides, only by rewriting the liking-sect-dependent, situationdependent keyword data base stored in memory means such as an EPPROM 38,up-to-date keywords can be treated immediately. Thereby, withoutlearning up-to-date keyword by heart, a user can always cope with theupdate of keywords.

Thus, according to the above arrangement, the load of a user concerningretrieval can be greatly reduced.

(5) Other Embodiments

Incidentally, in the above embodiment, the case of inputting the lifestage, age/sex, liking tendency and living scene as input items of auser has described, but the present invention is not only limited tothis case and the input items may be reduced to several of them or otheritems may be added.

Besides, in the above embodiment, the case where a keyword creationblock for information retrieval is provided inside the IRD 2 forreceiving a satellite broadcast has described, but the present inventionis not only limited to this and a keyword creation unit may be providedseparately.

Furthermore, in the above embodiment, the case where the presentinvention was applied to a device for retrieving a program of digitalsatellite broadcast has described, but the present invention is not onlylimited to this and is widely applicable to the keyword creation unit ofvarious information retrieval apparatus such as, e.g., for retrieving avast amount of information by means of internet and retrieving items ofpackage information in a compact disk or the like.

As mentioned above, according to the present invention, the habitualsituation characteristics and the degree of typical liking tendency of auser are calculated on the basis of answers on daily items of the user,typical situation dependent keyword(s) of the user in one or moreindividual typical situations previously prepared is(are) created inaccordance with the degree of typical liking tendency of the user andtypical situation dependent keyword(s) is(are) revised in accordancewith the habitual situation characteristics of the user, so thatkeyword(s) according to the actual situation of the user can be created.

While there has been described in connection with the preferredembodiments of the invention, it will be obvious to those skilled in theart that various changes and modifications may be aimed, therefore, tocover in the appended claims all such changes and modifications as fallwithin the true spirit and scope of the invention.

1. An apparatus for recommending one or more particular content thatmeet a user's preference, comprising: a user interface that enablesanswers to be inputted to a number of questions provided to a user;creating means for creating user preference information which includeshabitual situation characteristics of said user and a degree of atypical tendency of said user based on the answers, wherein the userpreference information is generated based on a group of retrievalkeywords such that one or more keywords correspond to one or moreattribute clusters, wherein the attribute clusters are formed from aplurality of users' answers, wherein the users' answers are values onattribute classification axes, indicating individual directionalities,and wherein the one more keywords are linked to the attribute clusters;a user's preference database for storing said created user preferenceinformation; a processor adapted to search content that may be ofinterest to the user as a function of said user's preference databaseand content guide information; and a display processor adapted togenerate a display signal representing a list of said searched content.2. The apparatus according to claim 1, said user interface enables saiduser to select one or more content from said displayed list viewing. 3.The apparatus according to claim 1, further comprising a decoder fordecoding compressed video and audio signals which represents saidselected one or more content.
 4. The apparatus according to claim 1,further comprising a content guide for receiving content guideinformation and content guide database for storing received contentguide information.
 5. The apparatus according to claim 1, furthercomprising a tuner for receiving a television signal and wherein saiduser's preference database tuning history data of said tuner.
 6. Amethod for recommending one or more particular content that meet auser's preference, comprising: inputting answers to a number ofquestions provided to a user; creating user preference information,which includes habitual situation characteristics of said user and adegree of a typical tendency of said user based on the answers, whereinthe user preference information is generated based on a group ofretrieval keywords such that one or more keywords correspond to one ormore attribute clusters, wherein the attribute clusters are formed froma plurality of users' answers, wherein the users' answers are values onattribute classification axes, indicating individual directionalities,and wherein the one more keywords are linked to the attribute clusters;storing said created user preference information; searching content thatmay be of interest to the user as a function of said user preferenceinformation and content guide information; and generating a displaysignal representing a list of said searched content.
 7. The methodaccording to claim 6, further comprising selecting one or more contentfrom said displayed list viewing.
 8. The method according to claim 6,further comprising decoding compressed video and audio signals whichrepresents said selected one or more content.
 9. The method according toclaim 6 further comprising receiving content guide information andcontent guide database for storing received content guide information.10. The method according to claim 6, further comprising receiving atelevision signal and wherein said user's preference database tuninghistory data of said tuner.